Invited Iceis Tanca Orsi

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Invited Iceis Tanca Orsi

  1. 1. the Context-ADDICT projectOntology driven,context-aware query distributionfor on-the-fly data-integrationLetizia Tanca and Giorgio Orsi
  2. 2. Data Integration: State of the art the Context-ADDICT project Dipartimento di Elettronica e Informazione
  3. 3. …the future the Context-ADDICT project Dipartimento di Elettronica e Informazione
  4. 4. 4OverviewAn ontology-driven solution for dynamic data integration, within a scenario where: data sources are not known a-priori user queries are dealt with in a context-aware fashion information fruition is fostered by  handing it to the user in a semantics-aware, integrated fashion  eliminating non-interesting information, thus reducing the “information noise”  controlling the problem’s dimension via context-based reduction of the current information spaceWe propose a DL language, CA-DL, which can uniformly represent the application domain and the contextQueries are issued to the system in SPARQL and translated into CA-DL for internal processing the Context-ADDICT project Dipartimento di Elettronica e Informazione
  5. 5. Context-ADDICT(joint work with C. Bolchini, E. Quintarelli and F. A. Schreiber)Features Context-aware data/ontology tailoring [5] Ontology-driven, on-the-fly data integration of heterogeneous and dynamic data sources Multimodal access to resources Focus on small and mobile devices (sensors, mobile phones, custom embedded-systems)Applications Urban mobility Automotive, e-Health Logistics Energy Production Automation Automated and Personalized Advertisement Personal Information Systems the Context-ADDICT project Dipartimento di Elettronica e Informazione
  6. 6. Context-ADDICT : context-aware integration of the 6overall information collected from the data sources[MDM06]On-the-fly data integration + data reduction via tailoring the Context-ADDICT project Dipartimento di Elettronica e Informazione
  7. 7. 7 Modeling context: the CDT• An orthogonal context model, which can be adopted for any application (data tailoring, application and service adaptivity and fine-tuning, sensor queries…)• Single contexts are defined as subtrees of a Context Tree, representing the contexts currently envisaged for that particular application• Fine granularity, semantics- based … the Context-ADDICT project Dipartimento di Elettronica e Informazione
  8. 8. Domain OntologyDomain Ontology:• Supplies to the absence of a DB “global schema”• Shared and commonly agreed• Must be decidable and efficiently computable  CA-DL the Context-ADDICT project Dipartimento di Elettronica e Informazione
  9. 9. Data Sources: Semantic Extraction Data Source Ontology: • Semantic Extraction: semantic ontology + structural ontology • Models structural/semantic independence (the different models can be used separately) the Context-ADDICT project Dipartimento di Elettronica e Informazione
  10. 10. CDT  domain ontology  source ontologies the Context-ADDICT project Dipartimento di Elettronica e Informazione
  11. 11. Relevant areas, or projectionsProjection:• is the set of relevant data for a given user in a given context• projected from the ADO to the data sources• is context-aware• possibly materialized on the user device the Context-ADDICT project Dipartimento di Elettronica e Informazione
  12. 12. Our problem the Context-ADDICT project Dipartimento di Elettronica e Informazione
  13. 13. A closer look the Context-ADDICT project Dipartimento di Elettronica e Informazione
  14. 14. CA-DLCA-DL is used to create mappings between data sources and application domain ontologies and to represent the application context.CA-DL corresponds to a strict subset of OWL2, tailored to be rewritable from/to SPARQL syntax and to express both GAV and LAV mappings.A SPARQL query is issued to the system, and:• translated into CA-DL• transformed by adapting it to the current user context• handed over to the query-rewriting algorithm(s) which distribute it to the suitable data sources (i.e. when alternative data-sources are available)• translated into the data-source language(s) by means of automatically generated wrappers the Context-ADDICT project Dipartimento di Elettronica e Informazione
  15. 15. In CA-DLNo unions, keeping the complexity of the rewriting process within PTIME, and only allowing LAV mappings which involve intersections of concepts: in a CA-DIS the queries are highly heterogeneous and the mappings are often computed on-the-fly.No universal quantification: because GAV mappings rewrite the complex mapping into SPARQL syntax, where currently it is not possible to express general universal restrictions. Only special form of universal restriction: property range definitions where the concept N is the range of the property R. the Context-ADDICT project Dipartimento di Elettronica e Informazione
  16. 16. The CDT for the insurance companyapplication the Context-ADDICT project Dipartimento di Elettronica e Informazione
  17. 17. The CDT ontology the Context-ADDICT project Dipartimento di Elettronica e Informazione
  18. 18. The application domain ontology the Context-ADDICT project Dipartimento di Elettronica e Informazione
  19. 19. A context and its relevant area the Context-ADDICT project Dipartimento di Elettronica e Informazione
  20. 20. The application domain ontology manufacturer haspolicy expectsreceipt hasBrand Mname policy vehicle hasName customer receipt man hasclaim envisages hasriskclass motorcycle driver riskcar woman payment Haspayment drives high low claim mid Relevant area for context c1 the Context-ADDICT project Dipartimento di Elettronica e Informazione
  21. 21. The data sources and their semantic ontologiesDS1: Customer(id, name, ownesMotorbikePlateNumber) Motorbike(motorbikePlateNumber, manufacturer, model) the Context-ADDICT project Dipartimento di Elettronica e Informazione
  22. 22. The data sources and their semantic ontologiesDS2:Client(id, fullName, riskClass, gender)RiskClass(id, description) the Context-ADDICT project Dipartimento di Elettronica e Informazione
  23. 23. The mapping ontology the Context-ADDICT project Dipartimento di Elettronica e Informazione
  24. 24. Context-aware queries for context c1q(x,w)  Customer(x), drives(x, y), hasBrand(y, z), hasMname(z, w)This query correctly retrieves all the customers who drive a car with their manufacturer’s names, since the requested concepts and roles are included in the relevant area for context c1q(x,y)  Customer(x), hasName(x, y)This query correctly retrieves all the customers with their names, since the requested concept and property are included in the relevant area for context c1q(x,z)  Customer(x), hasPolicy(x, y), envisages(y, z)The answer to his query is empty in context c1, since its relevant area does not include the roles hasPolicy and envisages the Context-ADDICT project Dipartimento di Elettronica e Informazione
  25. 25. Context-aware queries: Context c1 q(x,y)  Customer(x), hasName(x,y)• The query is distributed to the datasources D1 and D2, after a reasoning step, through the mapping ontology.• The concept DS1:Customer is mapped (via LAV mappings) to an anonymous concept of the domain ontology containing women who drive motorbikes. The data property ado:hasName is mapped to the data property DS1:name• The concept ado:Customer is mapped (via GAV mapping) to and to an anonymous concept containing DS2:Client who has male gender with high risk class. The data property ado:hasName is mapped to the dataproperty DS2:fullname the Context-ADDICT project Dipartimento di Elettronica e Informazione
  26. 26. The data sources and their semantic ontologiesDS1: Customer(id, name, ownesMotorbikePlateNumber) Motorbike(motorbikePlateNumber, manufacturer, model) SELECT id, name FROM CustomerNote: the customers here are only women !!DS2:Client(id, fullName, riskClass, gender)RiskClass(id, description) SELECT id, fullname FROM Client, RiskClass WHERE Client.riskClass=RiskClass.id AND RiskClass=“high” AND gender=“male” the Context-ADDICT project Dipartimento di Elettronica e Informazione
  27. 27. Conclusions and future workAn ontology-driven solution for dynamic data integration, where: data sources are not known a-priori user queries are dealt with in a context-aware fashionThe future: Performance evaluation, in terms of: • Recall/precision • Efficiency Usage of the same framework in an Internet of things scenario the Context-ADDICT project Dipartimento di Elettronica e Informazione
  28. 28. Some references … the Context-ADDICT project Dipartimento di Elettronica e Informazione
  29. 29. CA-DL axioms the Context-ADDICT project Dipartimento di Elettronica e Informazione

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